Systems and methods for optimizing insulin dosage

ABSTRACT

Embodiments of a testing method for diabetics to optimize their administered insulin dosage comprise collecting sampling sets of biomarker data, each sampling set comprising a sufficient plurality of non-adverse sampling instances and each sampling instance comprising an acceptable biomarker reading at a single point in time recorded upon compliance with adherence criteria, determining a biomarker sampling parameter from each sampling set, comparing the biomarker sampling parameter to a target biomarker range, calculating an insulin adjustment parameter associated with the biomarker sampling parameter if the biomarker sampling parameter falls outside the target biomarker range, adjusting the insulin dosage by the insulin adjustment parameter if the biomarker sampling parameter falls outside the target biomarker range and the insulin dosage does not exceed maximum dosage, and exiting the testing method if the adjusted insulin dosage is optimized. The insulin dosage is optimized when biomarker sampling parameters fall within the target biomarker range.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 12/818,310 filed Jun. 18, 2010, which is a continuation in partof U.S. patent application Ser. No. 12/643,338 filed Dec. 21, 2009,which claims priority to U.S. Provisional Application Ser. No.61/140,270 filed Dec. 23, 2008, each of which are incorporated byreference herein in their entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to diabetesmanagement, and particularly to methods and systems for diabetic personsto optimize their administered insulin dosage.

BACKGROUND

A disease which is long lasting or which reoccurs often is definedtypically as a chronic disease. Known chronic diseases include, amongothers, depression, compulsive obsession disorder, alcoholism, asthma,autoimmune diseases (e.g., ulcerative colitis, lupus erythematosus),osteoporosis, cancer, and diabetes mellitus. Such chronic diseasesrequire chronic care management for effective long-term treatment. Afteran initial diagnosis, one of the functions of chronic care management isthen to optimize a patient's therapy of the chronic disease.

In the example of diabetes mellitus, which is characterized byhyperglycemia resulting from inadequate insulin secretion, insulinaction, or both, it is known that diabetes manifests itself differentlyin each person because of each person's unique physiology that interactswith variable health and lifestyle factors such as diet, weight, stress,illness, sleep, exercise, and medication intake.

Biomarkers are patient biologically derived indicators of biological orpathogenic processes, pharmacologic responses, events or conditions(e.g., aging, disease or illness risk, presence or progression, etc.).For example, a biomarker can be an objective measurement of a variablerelated to a disease, which may serve as an indicator or predictor ofthat disease. In the case of diabetes mellitus, such biomarkers includemeasured values for glucose, lipids, triglycerides, and the like. Abiomarker can also be a set of parameters from which to infer thepresence or risk of a disease, rather than a measured value of thedisease itself. When properly collected and evaluated, biomarkers canprovide useful information related to a medical question about thepatient, as well as be used as part of a medical assessment, as amedical control, and/or for medical optimization.

For diabetes, clinicians generally treat diabetic persons according topublished therapeutic guidelines such as, for example, Joslin DiabetesCenter & Joslin Clinic, Clinical Guideline for PharmacologicalManagement of Type 2 Diabetes (2007) and Joslin Diabetes Center & JoslinClinic, Clinical Guideline for Adults with Diabetes (2008). Theguidelines may specify a desired biomarker value, e.g., a fasting bloodglucose value of less than 100 mg/dl, or the clinician can specify adesired biomarker value based on the clinician's training and experiencein treating patients with diabetes.

However, such guidelines do not specify biomarker collection proceduresfor parameter adjustments to support specific therapies used inoptimizing a diabetic person's therapy. Subsequently, diabetic personsoften must measure their glucose levels with little structure forcollection and with little regard to lifestyle factors. Specifically, ahost of issues have been identified for the titration of basal insulin.The root cause of these issues is a lack of a centralized location thattells the patient the specific dosage of insulin to take—both during thetitration optimization phase, as well as post optimization—daily use.The issues may include the following: patients taking only the amount onthe label attached to the packaging as originally prescribed by thephysician; patients eating before sampling their blood glucose renderingthe sample instance unusable or inappropriate; patients forgetting totake the samples at the appropriate time; patients refusing to take morethan the minimal amount due to fear; and not understanding theinstructions from the physician.

It is desirable to include a parameterized testing method for theimplementation of most known basal rate titration algorithms, permittingthe creation of structured centralized testing procedures to assist thepatient in insulin titration.

SUMMARY

It is against the above background that the present testing methodembodiments suitable for diabetic persons to optimize their administeredinsulin dosage are provided. These embodiments for optimizing thetitration of insulin, specifically, basal insulin, helps a patient andphysician determine an insulin level that consistently results infasting blood glucose values within a predetermined range, and withoutadverse events (e.g., hypoglycemic events or hyperglycemic events ofvarying severity) occurring at any time of the day. The testing methodembodiments provide customized systems and methods which providesguidance and confidence to patient during titration of insulin. Thetesting methods benefit physicians by providing a monitoredimplementation of their standardized practices, which yields higherconfidence in the outcome of the titration. Additionally, the testingmethods are also anticipated to reduce the cost of optimization byreducing the number of required office visits necessary to support theperson in conducting the testing method.

Embodiments of the disclosure can be implemented, for example, asfollows: a paper tool; diabetes software integrated into a collectiondevice such as a blood glucose meter; diabetes software integrated intoa personal digital assistant, handheld computer, or mobile phone;diabetes software integrated into a device reader coupled to a computer;diabetes software operating on a computer such as a personal computer;and diabetes software accessed remotely through the internet.

In one embodiment, a testing method suitable for a diabetic person tooptimize an administered insulin dosage includes providing a structuredcollection procedure from a server of a health care provider to acollection device configured to guide the diabetic person through thestructured collection procedure and optimize the administered insulindosage. The collection device comprises a meter configured to measureone or more selected biomarkers, a processor disposed inside the meterand coupled to a memory, wherein the memory stores the structuredcollection procedure provided from the server, and software havinginstructions that when executed by the processor causes the processor toinstruct the diabetic person to collect one or more sampling sets ofbiomarker data in accordance with the structured collection procedure.The server is a central repository for a plurality of the structuredcollection procedures. The method further includes executing thesoftware on the collection device and according to instructions andtiming provided by the structured collection procedure: collecting oneor more sampling sets of biomarker data. Each of the one or moresampling sets comprises a plurality of sampling instances recorded overa collection period and each sampling instance of the plurality ofsampling instances comprises an acceptable biomarker reading recordedupon compliance with one or more acceptance criterion based on at leastone of a schedule-based action or a patient-based action and that isapplied to the one or more sampling sets of biomarker data. After eachcollection of the one or more sampling sets of biomarker data, saidprocessor performs the processes of: determining a biomarker samplingparameter from the one or more sampling sets of biomarker data wherebyonly these biomarker data are considered which are in compliance withthe one or more acceptance criterion, comparing the biomarker samplingparameter to a target biomarker range, calculating an insulin adjustmentparameter associated with the biomarker sampling parameter in responseto the biomarker sampling parameter falling outside the target biomarkerrange, adjusting an insulin dosage by an amount of the insulinadjustment parameter in response to the biomarker sampling parameterfalling outside the target biomarker range and in response to theinsulin dosage not exceeding maximum dosage, and exiting the testingmethod in response to the adjusted insulin dosage being optimized as anoptimized insulin dosage such that the optimized insulin dosage isadministered as the administered insulin dosage, otherwise repeatingwith a next sampling set of the one or more sampling sets of biomarkerdata, the optimized insulin dosage being achieved when the one or morebiomarker sampling parameters fall within the target biomarker range.

In another embodiment, a testing method suitable for a diabetic personto optimize an administered insulin dosage includes providing astructured collection procedure from a server of a health care providerto a collection device configured to guide the diabetic person throughthe structured collection procedure and optimize the administeredinsulin dosage. The collection device comprises a meter configured tomeasure one or more selected biomarkers, a processor disposed inside themeter and coupled to a memory, wherein the memory stores the structuredcollection procedure provided from the server, and software havinginstructions that when executed by the processor causes the processor toinstruct the diabetic person to collect one or more sampling sets ofbiomarker data in accordance with the structured collection procedure.The server is a central repository for a plurality of the structuredcollection procedures. The method further includes collecting one ormore sampling sets of biomarker data by the collection device. Each ofthe one or more sampling sets comprises a plurality of samplinginstances recorded over a collection period and each sampling instanceof the plurality of sampling instances comprises an acceptable biomarkerreading recorded upon compliance with one or more acceptance criterionbased on at least one of a schedule-based action or a patient-basedaction and that is applied to the one or more sampling sets of biomarkerdata. The one or more acceptance criterion require collection of theacceptable biomarker reading by the diabetic patient at a time requiredby the collection device. After each collection of the one or moresampling sets of biomarker data, said processor performs the processesof: determining a biomarker sampling parameter from the one or moresampling sets of biomarker data whereby only these biomarker data areconsidered which are in compliance with the one or more acceptancecriterion, comparing the biomarker sampling parameter to a targetbiomarker range, calculating an insulin adjustment parameter associatedwith the biomarker sampling parameter in response to the biomarkersampling parameter falling outside the target biomarker range, adjustingan insulin dosage by an amount of the insulin adjustment parameter inresponse to the biomarker sampling parameter falling outside the targetbiomarker range and in response to the insulin dosage not exceedingmaximum dosage, and exiting the testing method in response to theadjusted insulin dosage being optimized as an optimized insulin dosagesuch that the optimized insulin dosage is administered as theadministered insulin dosage, otherwise repeating with a next samplingset of the one or more sampling sets of of biomarker data, the optimizedinsulin dosage being achieved when the one or more biomarker samplingparameters fall within the target biomarker range.

These and additional features provided by the embodiments describedherein will be more fully understood in view of the following detaileddescription, in conjunction with the drawings. These and otheradvantages and features of the embodiments disclosed herein, will bemade more apparent from the description, drawings and claims thatfollow.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description of the embodiments of the presentdisclosure can be best understood when read in conjunction with thefollowing drawings, where like structure is indicated with likereference numerals.

FIG. 1 is a diagram showing a chronic care management system for adiabetes patient and a clinician along with others having an interest inthe chronic care management of the patient according to an embodiment ofthe present disclosure.

FIGS. 2 and 2A are diagrams showing embodiments of a system suitable forimplementing a structured testing method according to an embodiment ofthe present disclosure.

FIG. 3 shows a block diagram of a collection device embodiment accordingto the present disclosure.

FIG. 4 shows a depiction in tabular format of a data record embodimentcreated from using a structured testing method on the collection deviceof FIG. 3 according to the present disclosure.

FIGS. 5A-5D shows flow charts depicting testing methods for optimizingthe titration of insulin according to embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The present disclosure will be described below relative to variousillustrative embodiments. Those skilled in the art will appreciate thatthe present disclosure may be implemented in a number of differentapplications and embodiments and is not specifically limited in itsapplication to the particular embodiments depicted herein. Inparticular, the present disclosure will be discussed below in connectionwith diabetes management via sampling blood, although those of ordinaryskill will recognize that the present disclosure could be modified to beused with other types of fluids or analytes besides glucose, and/oruseful in managing other chronic diseases besides diabetes.

As used herein with the various illustrated embodiments described below,the follow terms include, but are not limited to, the followingmeanings.

The term “biomarker” can mean a physiological variable measured toprovide data relevant to a patient such as for example, a blood glucosevalue, an interstitial glucose value, an HbA1c value, a heart ratemeasurement, a blood pressure measurement, lipids, triglycerides,cholesterol, and the like.

The term “contextualizing” can mean documenting and interrelatingconditions that exists or will occur surrounding a collection of aspecific biomarker measurement. Preferably, data about documenting andinterrelating conditions that exists or will occur surrounding acollection of a specific biomarker are stored together with thecollected biomarker data and are linked to it. In particular, a furtherassessment of the collected biomarker data takes into account the dataabout documenting and interrelating conditions so that not only the dataas such are evaluated but also the link between data to which it iscontextualized. The data about documenting and interrelating conditionscan include for example information about the time, food and/orexercises which occurs surrounding a collection of a specific biomarkermeasurement and/or simultaneously thereto. For example, the context of astructured collection procedure according in an embodiment to thepresent disclosure can be documented by utilizing entry criterion forverifying a fasting state with the diabetic person before accepting abiomarker value during a Basal titration optimization focused testingprocedure.

The term “contextualized biomarker data” can mean the information on theinterrelated conditions in which a specific biomarker measurement wascollected combined with the measured value for the specific biomarker.In particular, the biomarker data are stored together with theinformation on the interrelated conditions under which a specificbiomarker measurement was collected and are linked thereto.

The term “biomarker sampling parameter” can mean the mathematicalmanipulation of the requisite number of collected, for example,non-adverse biomarker readings in a sampling set. The mathematicalmanipulation can be, for example, averaging sampling instances, summingthe sampling instances, performing a graphical analysis on the samplinginstances, performing a mathematical algorithm on the sampling set, orcombinations thereof.

The term “criteria” can mean one or more criterions, and can be at leastone or more of a guideline(s), rule(s), characteristic(s), anddimension(s) used to judge whether one or more conditions are satisfiedor met to begin, accept, and/or end one or more procedural steps,actions, and/or values.

The term “adherence” can mean that a person following a structuredcollection procedure performs requested procedural steps appropriately.For example, the biomarker data should be measured under prescribedconditions of the structured collection procedure. If then theprescribed conditions are given for a biomarker measurement theadherence is defined as appropriate. For examples, the prescribedconditions are time related conditions and/or exemplarily can includeeating of meals, taking a fasting sample, eating a type of meal with arequested window of time, taking a fasting sample at a requested time,sleeping a minimum amount of time, and the like. The adherence can bedefined as appropriate or not appropriate for a structured collectionprocedure, a group of sample instances, or a single data point of acontextualized biomarker data. Preferably, the adherence can be definedas appropriate or not appropriate by a range of a prescribedcondition(s) or by a selectively determined prescribed condition(s).Moreover the adherence can be calculated as a rate of adherencedescribing in which extent the adherence is given for a structuredcollection procedure or a single data point in particular of acontextualized biomarker data.

The term “adherence event” can mean when a person executing a structuredcollection procedure fails to perform a procedural step. For example, ifa person did not collect data when requested by the collection device,the adherence is determined as not appropriate resulting in an adherenceevent. In another example, adherence criteria could be a first criterionfor the patient to fast 6 hours and a second criterion for collecting afasting bG value at a requested time. In this example, if the patientprovides the bG sampling at the requested time but fasted only 3 hoursbefore providing, then although the second adherence criterion is met,the first adherence criterion is not, and hence an adherence event forthe first criterion would occur.

The term “violation event” is a form of an adherence event in which theperson executing the structured collection (testing) procedure(protocol) does not administer a therapeutic at a recommended time, doesnot administer a recommended amount, or both.

The term “adherence criterion” can include adherence and can mean abasis for comparison (e.g., assessment) of a value/information relatedto a measured value and/or a calculated value with a definedvalue/information, or defined range of the values, wherein based on thecomparison, data can be accepted with approval and positive reception.Adherence criterion can be applied to contextualized biomarker data sothat a biomarker data can be accepted depending on a comparison of thecontextualized data regarding the documentation and related conditionsthat exists, or occur, during the collection of the specific biomarker.Adherence criterion can be akin to a sanity check for a given piece ofinformation, or group of information. Preferably, the adherencecriterion can be applied to group of data, or information, and can berejected if the adherence criterion is not fulfilled. In particular,such rejected data are then not used for further calculations thatprovide a therapy recommendation. Mainly, the rejected data can only beused to assess the adherence and/or to automatically trigger at leastone further action. For example, such a triggered action can prompt theuser to follow a structured collection procedure, or a single requestedaction, so that the adherence criterion can be fulfilled.

The adherence criterion can be also applied to a single datapoint/information so that, for instance, a biomarker datum can beaccepted depending on a comparison of the contextualized data regardingthe documentation and related conditions that exists, or occur, duringthe collection of the specific biomarker. If the adherence criterion isapplied only to a single data point, the adherence criteria can beconstrued as an “acceptance criterion.”

The term “acceptance criterion,” therefore, can include an adherencecriterion applied to a single data point but can also include furthercriteria which can be applied to a single data point. A single datapoint/information can be then accepted depending on contextualized dataand, in addition, depending on conditions and/or results of ameasurement of that specific biomarker. For example, if a measurementerror is detected, the biomarker reading can be rejected because theacceptance criterion cannot be fulfilled, e.g., due to an under-dosedetection, or other measurement errors, which can occur and can bedetected by the system. Moreover, other criteria which define a specificrange in which a measured value can be located can be defined as anacceptance criterion of a single data point/information. The acceptancecriterion can be applied to contextualized biomarker data so that asingle data point/information can be accepted depending oncontextualized data regarding the documentation and related conditionsthat exists, or occur, during the collection of the specific biomarkerand a comparison (e.g., assessment) of these data with a definedvalue/information or defined range(s) of the value for contextualizeddata.

Moreover, the acceptance criterion can include additional criteriarelated to measurement errors and/or defined ranges of measured valuesas described above. As used herein, a biomarker, or event value, can be“acceptable” if the user follows the appropriate and recommended steps(i.e., adherence), and, in a preferred embodiment, the resulting dataare within a predicted range. For example, before a sample is taken, theacceptance criteria can establish whether the steps leading up to takingof the sample were accomplished. For example, the processor in responseto a request displays the question, “Have you been fasting for the last8 hours?,” wherein a “Yes” response received by the processor via theuser interface meets the acceptance criterion for this step. In anotherexample, after the sample is taken, the processor can assess thereceived data for reasonableness using other acceptance criterion(s).For example, based on prior data, a fasting bG sample should be between120-180 mg/dl, but the received value was of 340 mg/dl, and thus failssuch acceptance criteria since it is outside the predefined range for anacceptable value. In such an example, the processor could prompt for anadditional sample. If the re-sampling fails too (i.e., not between120-180 mg/dl), the assessment provided by the processor can be that thepatient has not fasted, and, thus, the processor, as instructed by theacceptance criterion upon a failing of the re-sampling, canautomatically extend the events in the schedule of events accordingly.In this specific example, the acceptance criterion can be based on anadherence criterion for a single data point (to be fasted) as a firstacceptance criterion in combination with a predefined range of the bloodglucose value which can be expected under that condition. Only if bothcriteria are fulfilled, the acceptance criterion overall can be met.

Furthermore, the acceptance criterion for a single datapoint/information can be derived from criteria which can be generatedbased on other data points/information. For example, if the adherencecriterion of the whole collection procedure during which a single datapoint is measured or the adherence criteria of neighboring values isunder a predefined threshold, the single data point cannot be accepted.In other words, the acceptance criterion of a single data point caninclude not only the adherence criterion for the measurement of thespecific biomarker reading but also the adherence criterion of furtherbiomarker readings or of the whole collection procedure. In addition,further criteria based on neighboring or related values of the specificsingle data point/information can be determined. For example, if apattern recognition is applied to biomarker readings with similarcontextualized data as related to the single data point/information, thesingle data point/information cannot then be acceptance if a reducedreliability is presumed based on the pattern recognition. For example,if a fasting blood glucose reading is detected as too high for thespecific person under the conditions of the contextualized data incomparison to biomarker readings under similar conditions, it can beassumed that data were wrongly recorded even if, for example, ameasurement error and/or an adherence event could not detected by thesystem itself. Consequently, the acceptance criterion can be defined bypredetermined criteria, for example, by predetermined values but can bealso defined dynamically based on data which can be generated during acollection procedure whereby specific criteria in particular values canbe derived therefrom. The acceptance criterion, therefore, can be usedto prove the reliability of a single data point/information so that onlythose values, which are significant and/or have a high reliability, canbe utilized for further calculation. As a consequence, the acceptancecriterion can ensure that a calculation of an insulin adjustmentparameter can be based only on these values which fulfill predefinedconditions that are essential for a correct insulin bolus calculationand that are accepted as values with a high reliability.

The term “data event request” can mean an inquiry for a collection ofdata at a single point in space-time defined by a special set ofcircumstances, for example, defined by time-related or not time-relatedevents.

The term “decentralized disease status assessment” can mean adetermination of the degree or extent of progression of a diseaseperformed by using a biomarker measurement of interest to deliver avalue without sending a sample to a laboratory for assessment.

The term “medical use case or question” can mean at least one or more ofa procedure, situation, condition, and/or question providing anuncertainty about the factuality of existence of some medical facts,combined with a concept that is not yet verified but that if true wouldexplain certain facts or phenomena. Medical use case or question can bealready deposited and stored in the system so that the diabetic personcan select between different medical use cases or questions.Alternatively, the medical use case or question can be defined by thediabetic person themselves.

The terms “focused”, “structured”, and “episodic” are used hereininterchangeably with the term “testing” and can mean a predefinedsequence in which to conduct the testing.

The terms “software” and “program” may be used interchangeably herein.

FIG. 1 shows a chronic care management system 10 for a diabetespatient(s) 12 and a clinician(s) 14 along with others 16 having aninterest in the chronic care management of the patient 12. Patient 12,having dysglycemia, may include persons with a metabolic syndrome,pre-diabetes, type 1 diabetes, type 2 diabetes, and gestationaldiabetes. The others 16 with an interest in the patient's care mayinclude family members, friends, support groups, and religiousorganizations all of which can influence the patient's conformance withtherapy. The patient 12 may have access to a patient computer 18, suchas a home computer, which can connect to a public network 50 (wired orwireless), such as the internet, cellular network, etc., and couple to adongle, docking station, or device reader 22 for communicating with anexternal portable device, such as a portable collection device 24. Anexample of a device reader is shown in the manual “Accu-Chek® Smart PixDevice Reader Diabetic person's Manual” (2008) available from RocheDiagnostics.

The collection device 24 can be essentially any portable electronicdevice that can function as an acquisition mechanism for determining andstoring digitally a biomarker value(s) according to a structuredcollection procedure, and which can function to run the structuredcollection procedure and the method of the present disclosure. Greaterdetails regarding various illustrated embodiments of the structuredcollection procedure are provided hereafter in later sections. In apreferred embodiment, the collection device 24 can be a self-monitoringblood glucose meter 26 or a continuous glucose monitor 28. An example ofa blood glucose meter is the Accu-Chek® Active meter, and the Accu-Chek®Aviva meter described in the booklet “Accu-Chek® Aviva Blood GlucoseMeter Owner's Booklet (2007), portions of which are disclosed in U.S.Pat. No. 6,645,368 B1 entitled “Meter and method of using the meter fordetermining the concentration of a component of a fluid” assigned toRoche Diagnostics Operations, Inc., which is hereby incorporated byreference. An example of a continuous glucose monitor is shown in U.S.Pat. No. 7,389,133 “Method and device for continuous monitoring of theconcentration of an analyte” (Jun. 17, 2008) assigned to RocheDiagnostics Operations, Inc., which is hereby incorporated by reference.

In addition to the collection device 24, the patient 12 can use avariety of products to manage his or her diabetes including: test strips30 carried in a vial 32 for use in the collection device 24; software 34which can operate on the patient computer 18, the collection device 24,a handheld computing device 36, such as a laptop computer, a personaldigital assistant, and/or a mobile phone; and paper tools 38. Software34 can be pre-loaded or provided either via a computer readable medium40 or over the public network 50 and loaded for operation on the patientcomputer 18, the collection device 24, the clinician computer/officeworkstation 25, and the handheld computing device 36, if desired. Instill other embodiments, the software 34 can also be integrated into thedevice reader 22 that is coupled to the computer (e.g., computers 18 or25) for operation thereon, or accessed remotely through the publicnetwork 50, such as from a server 52.

The patient 12 can also use for certain diabetes therapies additionaltherapy devices 42 and other devices 44. Additionally, therapy devices42 can include devices such as an ambulatory infusion pump 46, aninsulin pen 48, and a lancing device 51. An example of an ambulatoryinsulin pump 46 include but not limited thereto the Accu-Chek® Spiritpump described in the manual “Accu-Chek® Spirit Insulin Pump System PumpDiabetic person Guide” (2007) available from Disetronic Medical SystemsAG. The other devices 44 can be medical devices that provide patientdata such as blood pressure, fitness devices that provide patient datasuch as exercise information, and elder care device that providenotification to care givers. The other devices 44 can be configured tocommunicate with each other according to standards planned by Continua®Health Alliance. These therapy devices can be separate or integratedinto the collection devices and data processing devices describedherein.

The clinicians 14 for diabetes are diverse and can include e.g., nurses,nurse practitioners, physicians, endocrinologists, and other such healthcare providers. The clinician 14 typically has access to a cliniciancomputer 25, such as a clinician office computer, which can also beprovided with the software 34. A healthcare record system 27, such asMicrosoft® HealthVault™ and Google™ Health, may also be used by thepatient 12 and the clinician 14 on computers 18, 25 to exchangeinformation via the public network 50 or via other network means (LANs,WANs, VPNs, etc.), and to store information such as collection data fromthe collection device 24, handheld collection device 36, blood glucosemonitor 28, etc. to an electronic medical record of the patient e.g.,EMR 53 (FIG. 2A) which can be provided to and from computer 18, 25and/or server 52.

Most patients 12 and clinicians 14 can interact over the public network50 with each other and with others having computers/servers 52. Suchothers can include the patient's employer 54, a third party payer 56,such as an insurance company who pays some or all of the patient'shealthcare expenses, a pharmacy 58 that dispenses certain diabeticconsumable items, a hospital 60, a government agency 62, which can alsobe a payer, and companies 64 providing healthcare products and servicesfor detection, prevention, diagnosis and treatment of diseases. Thepatient 12 can also grant permissions to access the patient's electronichealth record to others, such as the employer 54, the payer 56, thepharmacy 58, the hospital 60, and the government agencies 62 via thehealthcare record system 27, which can reside on the clinician computer25 and/or one or more servers 52. Reference hereafter is also made toFIG. 2.

FIG. 2 shows a system embodiment suitable for implementing a structuredtesting method according to an embodiment of the present disclosure,which in another embodiment can be a part of the chronic care managementsystem 10 and communicate with such components, via conventional wiredor wireless communication means. The system 41 can include the cliniciancomputer 25 that is in communication with a server 52 as well as thecollection device 24. Communications between the clinician computer 25and the server 52 can be facilitated via a communication link to thepublic network 50, to a private network 66, or combinations thereof. Theprivate network 66 can be a local area network or a wide area network(wired or wireless) connecting to the public network 50 via a networkdevice 68 such as a (web) server, router, modem, hub, and the likes.

In one embodiment, the server 52 can be a central repository for aplurality of structured collection procedures (or protocols) 70 a, 70 b,70 c, 70 d (referred to in combination or collectively as one or morestructured collection procedures 70), in which the details of a fewexemplary structured collection procedures are provided in latersections. The server 52, as well as the network device 68, can functionalso as a data aggregator for completed ones of the structuredcollection procedures 70 a, 70 b, 70 c, 70 d. Accordingly, in such anembodiment, data of a completed collection procedure(s) from acollection device of the patient 12 can then be provided from the server52 and/or network device 68 to the clinician computer 25 when requestedin response to retrieval for such patient data.

In one embodiment, one or more of the plurality of structured collectionprocedures 70 a, 70 b, 70 c, 70 d on the server 52 can be provided overthe public network 50, such as through a secure web interface 55 (FIG.2A, showing another embodiment of the system 41) implemented on thepatient computer 18, the clinician computer 25, and/or the collectiondevice 24. In another embodiment, the clinician computer 25 can serve asthe interface (wired or wireless) 72 between the server 52 and thecollection device 24. In still another embodiment, the structuredcollection procedures 70 a, 70 b, 70 c, 70 d, as well as software 34,may be provided on a computer readable medium 40 and loaded directed onthe patient computer 18, the clinician computer 25, and/or thecollection device 24. In still another embodiment, the structuredcollection procedures 70 a, 70 b, 70 c, 70 d may be provided pre-loaded(embedded) in memory of the collection device 24. In still otherembodiments, new/updated/modified structured collection procedures 70 a,70 b, 70 c, 70 d may be sent between the patient computer 18, theclinician computer 25, the server 52 and/or the collection device 24 viathe public network 50, the private network 66, via a direct deviceconnection (wired or wireless) 74, or combinations thereof. Accordingly,in one embodiment the external devices e.g., computer 18 and 25, can beused to establish a communication link 72, 74 between the collectiondevice 24 and still further electronic devices such as other remotePersonal Computer (PC), and/or servers such as through the publicnetwork 50, such as the Internet and/or other communication networks(e.g., LANs, WANs, VPNs, etc.), such as private network 66.

The clinician computer 25, as a conventional personalcomputer/workstation, can include a processor 76 which executesprograms, such as software 34, and such as from memory 78 and/orcomputer readable medium 40. Memory 78 can include system memory (RAM,ROM, EEPROM, etc.), and storage memory, such as hard drives and/or flashmemory (internal or external). The clinician computer 25 can alsoinclude a display driver 80 to interface a display 82 with the processor76, input/output connections 84 for connecting diabetic person interfacedevices 86, such as a keyboard and mouse (wired or wireless), andcomputer readable drives 88 for portable memory and discs, such ascomputer readable medium 40. The clinician computer 25 can furtherinclude communication interfaces 90 for connections to the publicnetwork 50 and other devices, such as collection device 24 (wired orwireless), and a bus interface 92 for connecting the above mentionedelectronic components to the processor 76. Reference hereafter is nowmade to FIG. 3.

FIG. 3 is a block diagram conceptually illustrating the portablecollection device 24 depicted in FIG. 2. In the illustrated embodiment,the collection device 24 can include one or more microprocessors, suchas processor 102, which may be a central processing unit comprising atleast one more single or multi-core and cache memory, which can beconnected to a bus 104, which may include data, memory, control and/oraddress buses. The collection device 24 can include the software 34,which provides instruction codes that causes a processor 102 of thedevice to implement the methods of the present disclosure that arediscussed hereafter in later sections. The collection device 24 mayinclude a display interface 106 providing graphics, text, and other datafrom the bus 104 (or from a frame buffer not shown) for display on adisplay 108. The display interface 106 may be a display driver of anintegrated graphics solution that utilizes a portion of main memory 110of the collection device 24, such as random access memory (RAM) andprocessing from the processor 102 or may be a dedicated graphicprocessing unit. In another embodiment, the display interface 106 anddisplay 108 can additionally provide a touch screen interface forproviding data to the collection device 24 in a well-known manner.

Main memory 110 in one embodiment can be random access memory (RAM), andin other embodiments may include other memory such as a ROM, PROM, EPROMor EEPROM, and combinations thereof. In one embodiment, the collectiondevice 24 can include secondary memory 112, which may include, forexample, a hard disk drive 114 and/or a computer readable medium drive116 for the computer readable medium 40, representing for example, atleast one of a floppy disk drive, a magnetic tape drive, an optical diskdrive, a flash memory connector (e.g., USB connector, Firewireconnector, PC card slot), etc. The drive 116 reads from and/or writes tothe computer readable medium 40 in a well-known manner. Computerreadable medium 40, represents a floppy disk, magnetic tape, opticaldisk (CD or DVD), flash drive, PC card, etc. which is read by andwritten to by the drive 116. As will be appreciated, the computerreadable medium 40 can have stored therein the software 34 and/orstructured collection procedures 70 a, 70 b, 70 c, and 70 d as well asdata resulting from completed collections performed according to one ormore of the collection procedures 70 a, 70 b, 70 c, and 70 d.

In alternative embodiments, secondary memory 112 may include other meansfor allowing the software 34, the collection procedures 70 a, 70 b, 70c, 70 d, other computer programs or other instructions to be loaded intothe collection device 24. Such means may include, for example, aremovable storage unit 120 and an interface connector 122. Examples ofsuch removable storage units/interfaces can include a program cartridgeand cartridge interface, a removable memory chip (e.g., ROM, PROM,EPROM, EEPROM, etc.) and associated socket, and other removable storageunits 120 (e.g. hard drives) and interface connector 122 which allowsoftware and data to be transferred from the removable storage unit 120to the collection device 24.

The collection device 24 in one embodiment can include a communicationmodule 124. The communication module 124 allows software (e.g., thesoftware 34, the collection procedures 70 a, 70 b, 70 c, and 70 d) anddata (e.g., data resulting from completed collections performedaccording to one or more of the collection procedures 70 a, 70 b, 70 c,and 70 d) to be transferred between the collection device 24 and anexternal device(s) 126. Examples of communication module 124 may includeone or more of a modem, a network interface (such as an Ethernet card),a communications port (e.g., USB, Firewire, serial, parallel, etc.), aPC or PCMCIA slot and card, a wireless transceiver, and combinationsthereof. The external device(s) 126 can be the patient computer 18, theclinician computer 25, the handheld computing devices 36, such as alaptop computer, a personal digital assistance (PDA), a mobile(cellular) phone, and/or a dongle, a docking station, or device reader22. In such an embodiment, the external device 126 may provided and/orconnect to one or more of a modem, a network interface (such as anEthernet card), a communications port (e.g., USB, Firewire, serial,parallel, etc.), a PCMCIA slot and card, a wireless transceiver, andcombinations thereof for providing communication over the public network50 or private network 66, such as with the clinician computer 25 orserver 52. Software and data transferred via communication module 124can be in the form of wired or wireless signals 128, which may beelectronic, electromagnetic, optical, or other signals capable of beingsent and received by communication module 124. For example, as is known,signals 128 may be sent between communication module 124 and theexternal device(s) 126 using wire or cable, fiber optics, a phone line,a cellular phone link, an RF link, an infrared link, othercommunications channels, and combinations thereof. Specific techniquesfor connecting electronic devices through wired and/or wirelessconnections (e.g. USB and Bluetooth, respectively) are well known in theart.

In another embodiment, the collection device 24 can be used with theexternal device 132, such as provided as a handheld computer or a mobilephone, to perform actions such as prompt a patient to take an action,acquire a data event, and perform calculations on information. Anexample of a collection device combined with such an external device 126provided as a hand held computer is disclosed in U.S. patent applicationSer. No. 11/424,757 filed Jun. 16, 2006 entitled “System and method forcollecting patient information from which diabetes therapy may bedetermined,” assigned to Roche Diagnostics Operations, Inc., which ishereby incorporated by reference. Another example of a handheld computeris shown in the diabetic person guide entitled “Accu-Chek® PocketCompass Software with Bolus Calculator Diabetic person Guide” (2007)available from Roche Diagnostics.

In the illustrative embodiment, the collection device 24 can provide ameasurement engine 138 for reading a biosensor 140. The biosensor 140,which in one embodiment is the disposable test strip 30 (FIG. 1), isused with the collection device 24 to receive a sample such as forexample, of capillary blood, which is exposed to an enzymatic reactionand measured by electrochemistry techniques, optical techniques, or bothby the measurement engine 138 to measure and provide a biomarker value,such as for example, a blood glucose level. An example of a disposabletest strip and measurement engine is disclosed in U.S. Patent Pub. No.2005/0016844 A1 “Reagent stripe for test strip” (Jan. 27, 2005), andassigned to Roche Diagnostics Operations, Inc., which is herebyincorporated by reference. In other embodiments, the measurement engine138 and biosensor 140 can be of a type used to provide a biomarker valuefor other types of sampled fluids or analytes besides or in addition toglucose, heart rate, blood pressure measurement, and combinationsthereof. Such an alternative embodiment is useful in embodiments wherevalues from more than one biomarker type are requested by a structuredcollection procedure according to the present disclosure. In stillanother embodiment, the biosensor 140 may be a sensor with an indwellingcatheter(s) or being a subcutaneous tissue fluid sampling device(s),such as when the collection device 24 is implemented as a continuousglucose monitor (CGM) in communication with an infusion device, such aspump 46 (FIG. 1). In still another embodiments, the collection device 24can be a controller implementing the software 34 and communicatingbetween the infusion device (e.g., ambulatory infusion pump 46 andelectronic insulin pen 48) and the biosensor 140.

Data, comprising at least the information collected by the biosensor140, is provided by the measurement engine 138 to the processor 102which may execute a computer program stored in memory 110 to performvarious calculations and processes using the data. For example, such acomputer program is described by U.S. patent application Ser. No.12/492,667, filed Jun. 26, 2009, titled “Method, System, and ComputerProgram Product for Providing Both an Estimated True Mean Blood GlucoseValue and Estimated Glycated Hemoglobin (HbA1C) Value from StructuredSpot Measurements Of Blood Glucose,” and assigned to Roche DiagnosticsOperations, Inc., which is hereby incorporated by reference. The datafrom the measurement engine 138 and the results of the calculation andprocesses by the processor 102 using the data is herein referred to asself-monitored data. The self-monitored data may include, but notlimited thereto, the glucose values of a patient 12, the insulin dosevalues, the insulin types, and the parameter values used by processor102 to calculate future glucose values, supplemental insulin doses, andcarbohydrate supplement amounts as well as such values, doses, andamounts. Such data along with a date-time stamp 169 for each measuredglucose value and administered insulin dose value is stored in a datafile 145 of memory 110 and/or 112. An internal clock 144 of thecollection device 24 can supply the current date and time to processor102 for such use.

The collection device 24 can further provide a diabetic person interface146, such as buttons, keys, a trackball, touchpad, touch screen, etc.for data entry, program control and navigation of selections, choicesand data, making information requests, and the likes. In one embodiment,the diabetic person interface 146 can comprises one or more buttons 147,149 for entry and navigation of the data provided in memory 110 and/or112. In one embodiment, the diabetic person can use one or more ofbuttons 147, 149 to enter (document) contextualizing information, suchas data related to the everyday lifestyle of the patient 12 and toacknowledge that prescribed tasks are completed. Such lifestyle data mayrelate to food intake, medication use, energy levels, exercise, sleep,general health conditions and overall well-being sense of the patient 12(e.g., happy, sad, rested, stressed, tired, etc.). Such lifestyle datacan be recorded into memory 110 and/or 112 of the collection device 24as part of the self-monitored data via navigating through a selectionmenu displayed on display 108 using buttons 147, 149 and/or via a touchscreen diabetic person interface provided by the display 108. It is tobe appreciated that the diabetic person interface 146 can also be usedto display on the display 108 the self monitored data or portionsthereof, such as used by the processor 102 to display measured glucoselevels as well as any entered data.

In one embodiment, the collection device 24 can be switched on bypressing any one of the buttons 147, 149 or any combination thereof. Inanother embodiment, in which the biosensor 140 is a test-strip, thecollection device 24 can be automatically switched on when thetest-strip is inserted into the collection device 24 for measurement bythe measurement engine 138 of a glucose level in a sample of bloodplaced on the test-strip. In one embodiment, the collection device 24can be switched off by holding down one of the buttons 147, 149 for apre-defined period of time, or in another embodiment can be shut downautomatically after a pre-defined period of non-use of the diabeticperson interface 146.

An indicator 148 can also be connected to processor 102, and which canoperate under the control of processor 102 to emit audible, tactile(vibrations), and/or visual alerts/reminders to the patient of dailytimes for bG measurements and events, such as for example, to take ameal, of possible future hypoglycemia, and the likes. A suitable powersupply 150 is also provided to power the collection device 24 as is wellknown to make the device portable.

As mentioned above previously, the collection device 24 may bepre-loaded with the software 34 or by provided therewith via thecomputer readable medium 40 as well as received via the communicationmodule 124 by signal 128 directly or indirectly though the externaldevice 132 and/or network 50. When provided in the latter matter, thesoftware 34 when received by the processor 102 of the collection device24 is stored in main memory 110 (as illustrated) and/or secondary memory112. The software 34 contains instructions, when executed by theprocessor 102, enables the processor to perform the features/functionsof the present disclosure as discussed herein in later sections. Inanother embodiment, the software 34 may be stored in the computerreadable medium 40 and loaded by the processor 102 into cache memory tocause the processor 102 to perform the features/functions of thedisclosure as described herein. In another embodiment, the software 34is implemented primarily in hardware logic using, for example, hardwarecomponents such as application specific integrated circuits (ASICs).Implementation of the hardware state machine to perform thefeature/functions described herein will be apparent to persons skilledin the relevant art(s). In yet another embodiment, the disclosure isimplemented using a combination of both hardware and software.

In an example software embodiment of the disclosure, the methodsdescribed hereafter can be implemented in the C++ programming language,but could be implemented in other programs such as, but not limited to,Visual Basic, C, C#, Java or other programs available to those skilledin the art. In still other embodiment, the program 34 may be implementedusing a script language or other proprietary interpretable language usedin conjunction with an interpreter. Reference hereafter is also made toFIG. 4.

FIG. 4 depicts in tabular form a data file 145 containing data records152 of self-monitored data 154 resulting from a structured collectionprocedure according to an embodiment of the present disclosure. The datarecords 152 (e.g., rows) along with the self-monitoring data 154 (e.g.,various one of the columns) can also provide associated therewithcontextual information 156 (e.g., other various ones of the columns aswell as via row and column header information). Such contextualinformation 156 can be collected either automatically, such as forexample via input received automatically from the measurement engine,the biosensor, and/or any one of the other devices, or via inputreceived from the diabetic person interface which was manually enteredby the patient in response to a collection request (e.g., a questiondisplayed by the processor 102 on the display 108) during the structuredcollection procedure. Accordingly, as such contextual information 156can be provided with each data record 152 in a preferred embodiment,such information is readily available to a physician and no furthercollection of such information is necessarily needed to be providedagain by the patient either manually or orally after completing thestructured collection procedure. In another embodiment, if suchcontextual information 156 and/or additional contextual information iscollected after completion of a structured collection procedureaccording to the present disclosure, such information may be provided inthe associated data file and/or record 145, 152 at a later time such asvia one of the computers 18, 25. Such information would then beassociated with the self-monitored data in the data file 145, and thuswould not need to be provided again orally or manually. Such a processin the latter embodiment may be needed in the situation where thestructured collection procedure is implemented as or partly as a papertool 38 which is used with a collection device incapable of running thesoftware 34 implementing such a structured collection procedure.

It is to be appreciated that the date file 145 (or portions thereof,such as only the self-monitored data 154) can be sent/downloaded (wiredor wireless) from the collection device 24 via the communication module124 to another electronic device, such the external device 132 (PC, PDA,or cellular telephone), or via the network 50 to the clinician computer25. Clinicians can use diabetes software provided on the cliniciancomputer 25 to evaluate the received self-monitored data 154 as well asthe contextual information 156 of the patient 12 for therapy results. Anexample of some of the functions which may be incorporated into thediabetes software and which is configured for a personal computer is theAccu-Chek® 360 Diabetes Management System available from RocheDiagnostics that is disclosed in U.S. patent application Ser. No.11/999,968 filed Dec. 7, 2007, titled “METHOD AND SYSTEM FOR SETTINGTIME BLOCK,” and assigned to Roche Diagnostics Operations, Inc., whichis hereby incorporated by reference.

In a preferred embodiment, the collection device 24 can be provided asportable blood glucose meter, which is used by the patient 12 forrecording self-monitored data comprising insulin dosage readings andspot measured glucose levels. Examples of such bG meters as mentionedabove previously include but are not limited to, the Accu-Chek® Activemeter and the Accu-Chek® Aviva system both by Roche Diagnostics, Inc.which are compatible with the Accu-Chek® 360° Diabetes managementsoftware to download test results to a personal computer or theAccu-Chek® Pocket Compass Software for downloading and communicationwith a PDA. Accordingly, it is to be appreciated that the collectiondevice 24 can include the software and hardware necessary to process,analyze and interpret the self monitored data in accordance withpredefined flow sequences (as described below in detail) and generate anappropriate data interpretation output. In one embodiment, the resultsof the data analysis and interpretation performed upon the storedpatient data by the collection device 24 can be displayed in the form ofa report, trend-monitoring graphs, and charts to help patients managetheir physiological condition and support patient-doctor communications.In other embodiments, the bG data from the collection device 24 may beused to generated reports (hardcopy or electronic) via the externaldevice 132 and/or the patient computer 18 and/or the clinician computer25.

The collection device 24 can further provide the diabetic person and/orhis or her clinician with at least one or more of the possibilitiescomprising: a) editing data descriptions, e.g. the title and descriptionof a record; b) saving records at a specified location, in particular indiabetic person-definable directories as described above; c) recallingrecords for display; d) searching records according to differentcriteria (date, time, title, description etc.); e) sorting recordsaccording to different criteria (e.g., values of the bG level, date,time, duration, title, description, etc.); f) deleting records; g)exporting records; and/or h) performing data comparisons, modifyingrecords, excluding records as is well known.

As used herein, lifestyle can be described in general as a pattern in anindividual's habits such as meals, exercise, and work schedule. Theindividual additionally may be on medications such as insulin therapy ororals that they are required to take in a periodic fashion. Influence ofsuch action on glucose is implicitly considered by the presentdisclosure.

It is to be appreciated that the processor 102 of the collection device24 can implement one or more structured collection procedures 70provided in memory 110 and/or 112. Each structured collection procedure70 in one embodiment can be stand-alone software, thereby providing thenecessary program instructions which when executed by the processor 102causes the processor to perform the structure collection procedure 70 aswell as other prescribed functions. In other embodiments, eachstructured collection procedure 70 can be part of the software 34, andcan be then be selectively executed by the processor 102 either viareceiving a selection from a menu list provided in the display 108 fromthe diabetic person interface 146 in one embodiment or via activation ofa particular diabetic person interface, such as a structured collectionprocedure run mode button (not shown) provided to the collection device24 in another embodiment. It is to be appreciated that the software 34,likewise, provides the necessary program instructions which whenexecuted by the processor 102 causes the processor to perform thestructure collection procedure 70 as well as other prescribed functionsof the software 34 discussed herein. One suitable example of having aselectable structured collection procedure provided as a selectable modeof a collection meter is disclosed by in U.S. patent application Ser.No. 12/491,523, filed Jun. 25, 2009, titled “Episodic Blood GlucoseMonitoring System With An Interactive Graphical Diabetic personInterface And Methods Thereof,” assigned to Roche DiagnosticsOperations, Inc., which is hereby incorporated by reference.

In still another embodiment, a command instruction can be sent from theclinician computer 25 and received by the processor 102 via thecommunication module 124, which places the collection device 24 in acollection mode which runs automatically the structured collectionprocedure 70. Such a command instruction may specify which of the one ormore structured collection procedures to run and/or provide a structuredcollection procedure to run. In still another embodiment, a list ofdefined medical use cases or medical questions can be presented on thedisplay 108 by the processor 102, and a particular structured collectionprocedure 70 can be automatically chosen by the processor 102 from aplurality of structured collection procedures (e.g., procedures 70 a, 70b, 70 c, and 70 d) depending on the selection of the defined medical usecases or medical questions received by the processor 102 via thediabetic person interface 146.

In still another embodiment, after selection, the structured collectionprocedure(s) 70 can be provided through the computer readable mediume.g., 40 and loaded by the collection device 24, downloaded fromcomputer 18 or 25, the other device(s) 132, or server 52. Server 52, forexample, may be a healthcare provider or company providing suchpre-defined structured collection procedures 70 for downloadingaccording to a selected defined medical use case or question. It is tobe appreciated that the structured collection procedure(s) 70 may bedeveloped by a healthcare company (e.g. company 64) and implemented viathe public network 50 through a webpage and/or made available fordownloading on server 52, such as illustrated in FIG. 2. In still otherembodiments, notices that a new structured collection procedure 70 isavailable for use on the collection device 24 to help address aparticular use case/medical question that a diabetic person (e.g.,healthcare provider and patient) may have can be provided in anystandard fashion, such for via postal letters/cards, email, textmessaging, tweets, and the likes.

In some embodiments, as mentioned above previously, a paper tool 38 canperform some of the functions provided by the diabetes software 34. Anexample of some of the functions which may be incorporated into thediabetes software 34 and which is configured as a paper tool 38 is theAccu-Chek® 360 View Blood Glucose Analysis System paper form availablefrom Roche Diagnostics also disclosed in U.S. patent application Ser.No. 12/040,458 filed Feb. 29, 2007 entitled “Device and method forassessing blood glucose control,” assigned to Roche DiagnosticOperations, Inc., which is hereby incorporated by reference.

In still another embodiment, the software 34 can be implemented on thecontinuous glucose monitor 28 (FIG. 1). In this manner, the continuousglucose monitor 28 can be used to obtain time-resolved data. Suchtime-resolved data can be useful to identify fluctuations and trendsthat would otherwise go unnoticed with spot monitoring of blood glucoselevels and standard HbA1c tests. Such as, for example, low overnightglucose levels, high blood glucose levels between meals, and earlymorning spikes in blood glucose levels as well as how diet and physicalactivity affect blood glucose along with the effect of therapy changes.

In addition to collection device 24 and software 34, clinicians 14 canprescribe other diabetes therapy devices for patients 12 such as anambulatory insulin pump 46 as well as electronically based insulin pen48 (FIG. 1). The insulin pump 46 typically includes configurationsoftware such as that disclosed in the manual “Accu-Chek® Insulin PumpConfiguration Software” also available from Disetronic Medical SystemsAG. The insulin pump 46 can record and provide insulin dosage and otherinformation, as well as the electronically based insulin pen 48, to acomputer, and thus can be used as another means for providing biomarkerdata as requested by the structured collection procedure 70 (FIG. 2)according to the present disclosure.

It is to be appreciated that, and as mentioned above previously, one ormore of the method steps discussed hereafter can be configured as apaper tool 38 (FIG. 1), but preferably all the method steps arefacilitated electronically on system 41 (FIG. 2) or on any electronicdevice/computer, such as collection device 24, having a processor andmemory as a program(s) residing in memory. As is known, when a computerexecutes the program, instructions codes of the program cause theprocessor of the computer to perform the method steps associatedtherewith. In still other embodiments, some or all of the method stepsdiscussed hereafter can be configured on computer readable medium 40storing instruction codes of a program that, when executed by acomputer, cause the processor of the computer to perform the methodsteps associated therewith. These method steps are now discussed ingreater detail hereafter with reference made to FIG. 5A.

Testing Method Embodiments for Optimizing the Titration of Insulin

FIG. 5A provides an exemplary embodiment of testing methods foroptimizing the titration of insulin dosage, which thereby yield dosagesof insulin which maintain biomarker levels within a desired range. Inone embodiment, the titrated insulin may be basal insulin. Upon startingthe testing method, the dosage of insulin is typically the initialprescribed dosage, for example, the initial prescribed dosage listed onthe package. However, other dosages are contemplated depending on whatstage of the testing method, as the entry criteria may be consideredbefore every biomarker reading. Consequently, the initial dosage may bean adjusted dosage above the initial prescribed dosage, the maximumallowable dosage, or even the optimized dosage. It is contemplated thatthe testing method may be used to obtain the optimized insulin value, ormay be used post-optimization to verify that the insulin dosage is stilloptimal.

In the embodiments of FIG. 5A, the testing methods may optionallyrequire the consideration of entry criteria 510 before beginningcollection of the biomarker data. It is contemplated that the diabeticperson, the healthcare provider, or both may determine whether the entrycriteria are met. The entry criteria, which in some embodiments may beestablished by the healthcare provider, may relate to the age, weight,and medical history of the diabetic person. Consequently, the testingmethod may require a diabetic person to receive a check-up or physicalto ensure the diabetic person satisfies the entry criteria. Forinstance, the entry criteria may specify the fasting plasma glucose(FPG) level or glycolated hemoglobin level as determined by the HbA1ctest. The normal range for the HbA1c test is between 4-6% for peoplewithout diabetes, so the entry criteria may require values above about6%, or in exemplary embodiment, between about 7.5% to about 10%. As anadditional example of entry criteria, a fasting plasma glucose level ofat least about 140 mg/dl is required. The entry criteria may also setrequirements on weight or Body Mass Index (BMI). For example, therequired BMI may be greater than about 25 kg/m2, or between about 26kg/m2 to about 40 kg/m2. Additionally, the entry criteria may specify adesired age range (e.g., 30-70) or the number of years afflicted withdiabetes (e.g., at least 2 years). Moreover, while it is contemplatedthat the testing method is applicable to persons afflicted all types ofdiabetes, the entry criteria may limit the testing method to type 2diabetics. Furthermore, the entry criteria may center on the currentdiabetes treatment regimen of the diabetic person. For example, theentry criteria may require that the treatment regimen for the diabeticperson be limited to oral anti-diabetes medication i.e., no injectedinsulin. Additionally, the entry criteria may require that the diabeticperson not be ill or under stress. As stated above, while theembodiments of FIG. 5A are directed to the consideration of entrycriteria, the present testing methods do not require the considerationof entry criteria before collection of biomarker data. For example,referring to the additional embodiments of FIGS. 5B-D, the embodiment ofFIG. 5B requires the consideration of entry criteria; however, theembodiments of FIGS. 5C and 5D do not include such constraints.

Referring again to FIG. 5A, if the entry criteria are not met, thetesting method will not be initiated 515. The diabetic person orhealthcare provider may determine whether the entry criteria have beenmet, or the data processor may determine whether criteria have been met.If the entry criteria are met 510, then the diabetic person may commencewith the testing method. However, in some embodiments, it may berequired for the diabetic person satisfy adherence criteria 520 beforethe collection of biomarkers or the administration of insulin.

The adherence criteria 520 are the procedural requirements that thediabetic person must follow when conducting the testing method. To get aproper baseline for the biomarker readings, it may be beneficial toensure all readings are taken uniformly, i.e., at approximately the sametime of day for each sampling instance. Consequently, the adherencecriteria 520 may specify that biomarker collection or insulinadministration be conducted at the same time each day. To aid thediabetic person in satisfying the adherence criteria 520, the dataprocessor display prompt the diabetic patient with audio and/or visualreminders to collect their biomarker sample, and enable the diabeticpatient to set future reminders. In specific embodiments, the adherencecriteria 520 may also require that the diabetic person fast for a setperiod of time prior to collecting the biomarker reading. The adherencecriteria 520 may also be directed to determining whether the diabeticperson is taking the correct dosage of insulin. In additionalembodiments, the adherence criteria 520 may also require no recenthypoglycemic events or severe hypoglycemic events (low blood glucoseevents) a set period (e.g. one week) before the collection of biomarkerdata. Furthermore, the adherence criteria 520 may specify an exerciseregimen or eating regimen for the diabetic person. As used herein,“eating regimen” means the typical eating regimen of the diabetic personin terms of calories, carbohydrate intake and protein intake.

If the diabetic person fails to meet any or all of the adherencecriteria 520, the diabetic person may be informed, for example, by thedisplay of the blood glucose meter that they failed to meet theadherence criterion. If the diabetic person fails to meet the adherencecriteria 520, the data processor device may tag the adherence event orthe diabetic person may record the occurrence of the adherence event.After the adherence event is recorded, the testing method is typicallycontinued. However, if too many adherence events are recorded (e.g.,more than 4 within a sample period, more than 20 adherence events withinthe entirety of execution), then the testing method may be terminated525. Furthermore, the testing method may also evaluate adherence eventsdifferently. For example, there may be a tiered adherence eventassessment, wherein adherence events are weighted. In one or moreembodiments, if the adherence event does not impact the biomarker data,then it is not weighted as heavily as an adherence event that affectsthe biomarker data. For example, when a diabetic person fasts therequisite time period before taking a fasting blood glucose reading, butfails to record that the reading is a fasting blood glucose reading,this would be categorized diabetic as a less significant and therebylower weighted adherence event, because the recording error does notaffect the fasting blood glucose reading. In contrast, fasting less thanthe requisite period will impact the fasting blood glucose reading, andthus constitutes a more significant and thereby higher weightedadherence event.

If there is a violation event (e.g., a missed insulin administration),the testing method is more likely to be terminated than for an adherenceevent (e.g., fasting less than the required fasting period), because aviolation event impacts the testing method more significantly. Since thepresent testing method is directed to optimizing insulin administration,it stands to reason that missing an insulin dose would be a significantviolation event.

Like other instructions provided to the diabetic person throughout thetesting method, the entry criteria or the adherence criteria 520 may beprovided to the diabetic person via a paper instruction form, or adisplay unit on a data processing device or processor 102 as shown inFIG. 3. The data processing devices may be any electronic devicedescribed above. In one or more embodiments, the data processing devicemay be a computer or a blood glucose meter with a data processor andmemory units therein. In addition to listing the entry criteria,adherence criteria 520, or both, the data processing device may promptthe diabetic person to answer medical questions, wherein the answers tothe medical questions are used by the device to determine compliancewith the entry criteria, or adherence criteria 520. The data processingdevice may inform the diabetic person of the failure to comply with theentry criteria or adherence criteria 520. For example, the dataprocessing device may inform a diabetic person if subsequent samplinginstances are not taken around the same time as the first samplinginstance. The patient can record sampling instances or answer medicalquestions by entering the data event directly into a device or computer,wherein the processor 102 can store the information and provideadditional analysis depending on the parameters of the testing method.

Referring again to FIG. 5A, the diabetic person may begin collection ofone or more sampling sets of biomarker data. Each sampling set comprisesa sufficient plurality of non-adverse sampling instances recorded over acollection period, which means at least two sampling instances which arenot indicative of an adverse event e.g., a hypoglycemic or hyperglycemicevent. Each sampling instance 540 comprises a biomarker reading at asingle point in time. The collection period for the sampling set may bedefined as multiple sampling instances within a day, multiple samplinginstances within a week, multiple sampling instances within consecutiveweeks, or multiple sampling instances on consecutive days within a week.The biomarker may relate to the levels of glucose, triglycerides, lowdensity lipids, and high density lipids. In one exemplary embodiment,the biomarker reading is a blood glucose reading. In addition to thebiomarker reading, each sampling instance may comprise the biomarkerreading and other contextual data associated with the biomarker reading,wherein the contextual data is selected from the group consisting of thetime of collection, the date of collection, the time when the last mealwas consumed, affirmation that fasting has occurred for the requisiteperiod, and combinations thereof. In the exemplary embodiment of FIG.5B, the testing method occurs over a 7 day method which requires thediabetic patient to administer insulin 505 in the evening followed bythe collection of fasting blood glucose reading the following morning.In addition to the morning biomarker collection, the diabetic patientmay also be instructed to take an additional biomarker reading when thediabetic person is encountering the symptoms of hypoglycemia.

Referring again to FIG. 5A, upon collecting the biomarker reading, thereis a determination as to whether the biomarker reading indicates anadverse event 550. While the present discussion of adverse eventscenters on hypoglycemic events and severe hypoglycemic events 555, whichmay necessitate medical assistance, it is contemplated that the adverseevents may refer to undesirable levels of other biomarkers or medicalindicators, e.g., lipids levels, blood pressure levels, etc. In oneembodiment, this determination of adverse events may be performed bycomparing the biomarker reading to a low threshold, for example, thehypoglycemic event or severe hypoglycemic event 555 thresholds shown inTable 1 below. If the biomarker reading is below one or both of thesethresholds, then an adverse event may have occurred, and should berecorded as an adverse event, or specifically recorded as a hypoglycemicevent or severe hypoglycemic event 555. As described above, thisdetermination may be performed by a data processor unit, or may beentered manually by the diabetic person.

TABLE 1 Insulin Adjustment Parameter Blood Glucose Range (mg/di) (units)below 56 (severe hypoglycemic event) −2 to −4 56-72 (hypoglycemic event)0 73 to 100 (target biomarker range) 0 100-119 +2 120-139 +4 140-179 +6180 and above +8

If there is an adverse event (e.g., a severe hypoglycemic event 555), inone embodiment, the instructions or data processing device may recommendthat the diabetic person contacts their health care provider. In anotherembodiment, the system may automatically contact the health careprovider (HCP). In addition, an adverse event may optionally lead to adosage reduction. Referring to Table 1 above, if it is a hypoglycemicevent (between 56-72 mg/dl), the HCP may be contacted 650, but thedosage is not adjusted (See FIG. 5). However, if it is a severehypoglycemic event 555 (below 56 mg/dl), the dosage may be reduced bysome amount (640), for example, 2 units, 4 units, or another amount asdictated by the low biomarker reading. In specific embodiments, if therecorded adverse event is a second measured severe hypoglycemic event555 within the same day, the dosage is not reduced. In furtherembodiments, a data processing device may utilize an algorithm toautomatically reduce the insulin dosage and instruct the diabetic personof the reduced insulin dosage. Moreover, the data processing devicewhich collects the biomarker reading may automatically notify ahealthcare provider of the adverse event, for example, by an automatedemail or text message.

If the biomarker reading is not adverse, the next step depends onwhether or not the sampling set 560 has a sufficient number ofnon-adverse sampling instances. If only one sampling instance isrequired for the sampling set, then the biomarker sampling parameter maybe calculated at that point; however, as stated above, the sampling settypically requires a plurality or at least two sampling instances foreach sampling set. In exemplary embodiments, two or more samplinginstances taken on consecutive days are required for each sampling set.If multiple sampling instances are required, then the diabetic personmust continue to collect sampling instances.

Once the requisite number of sampling instances for the sampling set isobtained, the biomarker sampling parameter may be obtained 570. Thebiomarker sampling parameter may be determined by various algorithms ormethodologies. For example, it may be determined by averaging samplinginstances, summing the sampling instances, performing a graphicalanalysis on the sampling instances, performing a mathematical algorithmon the sampling set, or combinations thereof. In an exemplaryembodiment, sampling instances (i.e., biomarker readings) are collectedon at least three consecutive days, and the average of the threeconsecutive days is the biomarker sampling parameter.

After the biomarker sampling parameter is obtained, the value iscompared to a target biomarker range 580. As used herein, the targetbiomarker range 580 means an acceptable range of biomarker in thediabetic person, which thereby demonstrates that the insulin isproducing the desired physiological response. If the biomarker samplingparameter falls outside of the target biomarker range 580, then aninsulin adjustment parameter may be calculated 590. The insulinadjustment parameter is associated with and computed from the biomarkersampling parameter. Various methodologies and algorithms arecontemplated for calculating the insulin adjustment parameter. Forexample, the insulin adjustment parameter may be computed by locatingthe insulin adjustment parameter associated with the biomarker parameterin an insulin adjustment parameter lookup table (See Table 1 above). Asshown above in the exemplary insulin adjustment parameter lookup tableof Table 1, there may be multiple tiers which dictate how much theinsulin dosage should be adjusted. For example, a fasting glucose levelbelow 100 mg/dl but above 56 mg/dl will necessitate no adjustment to theinsulin dosage. The greater the deviation from the target range, thehigher the adjustment of insulin in units.

After determining the insulin adjustment parameter, the insulin dosagemay be adjusted by the amount of the insulin adjustment parameter, aslong as the insulin adjustment does not raise the insulin dosage abovethe maximum allowable dosage 600. The adjusted insulin dosage cannotexceed a maximum level set by the healthcare provider. Upon determiningthe adjusted insulin dosage value, the diabetic person may then beinstructed to collect at least one additional sampling set at theadjusted insulin dosage per the above described collection procedures.The biomarker sampling parameter, the insulin adjustment parameter, andthe adjusted insulin dosage may be computed manually by the diabeticperson or via a data processing device.

If the biomarker sampling parameter is within a target biomarker range580, there is no adjustment of the insulin dosage. Moreover, the insulindosage may be considered optimized depending on other applicablecriteria. Specifically, an insulin dosage may be considered optimized ifone biomarker sampling parameter is within a target biomarker range, orit may be considered optimized if at least two consecutive biomarkersampling parameters are within a target biomarker range 620. If theoptimization definition requires at least two consecutive biomarkersampling parameters within a target biomarker range, the diabetic personis then instructed to collect at least one additional sampling set atthe adjusted insulin dosage per the above described collectionprocedures. After the insulin dosage is considered optimized, thediabetic person is instructed to exit the testing method. After exitingthe testing method 530, the diabetic person may conduct further testingmethods to determine the future efficacy of the optimized dosage.

In alternative embodiments, the diabetic patient may be instructed toexit the testing method 530 if the diabetic person has been undergoingthe testing procedure for a long period, for example, 6 months orlonger. Additionally, as described above, if there are multipleadherence or violation events, then the test may be automaticallyterminated by the data processing device or the diabetic patient may beinstructed to exit the testing method.

Having described the disclosure in detail and by reference to specificembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope defined in theappended claims. More specifically, although some aspects of the presentdisclosure are identified herein as preferred or particularlyadvantageous, it is contemplated that the present disclosure is notnecessarily limited to these preferred aspects of the disclosure.

All cited documents are, in relevant part, incorporated herein byreference; the citation of any document is not to be construed as anadmission that it is prior art with respect to the present disclosure.To the extent that any meaning or definition of a term in this writtendocument conflicts with any meaning or definition of the term in adocument incorporated by reference, the meaning or definition assignedto the term in this written document shall govern.

While particular embodiments of the present disclosure have beenillustrated and described, it would be obvious to those skilled in theart that various other changes and modifications can be made withoutdeparting from the spirit and scope of the disclosure. It is thereforeintended to cover in the appended claims all such changes andmodifications that are within the scope of this disclosure.

What is claimed is:
 1. A testing method suitable for a diabetic personto optimize an administered insulin dosage comprising: providing astructured collection procedure from a server of a health care providerto a collection device configured to guide the diabetic person throughthe structured collection procedure and optimize the administeredinsulin dosage, wherein the collection device comprises a meterconfigured to measure one or more selected biomarkers, a processordisposed inside the meter and coupled to a memory, wherein the memorystores the structured collection procedure provided from the server, andsoftware having instructions that when executed by the processor causesthe processor to instruct the diabetic person to collect one or moresampling sets of biomarker data in accordance with the structuredcollection procedure, wherein the server is a central repository for aplurality of the structured collection procedures; executing thesoftware on the collection device and according to instructions andtiming provided by the structured collection procedure: collecting oneor more sampling sets of biomarker data, wherein each of the one or moresampling sets comprises a plurality of sampling instances recorded overa collection period and each sampling instance of the plurality ofsampling instances comprises an acceptable biomarker reading recordedupon compliance with one or more acceptance criterion based on at leastone of a schedule-based action or a patient-based action and that isapplied to the one or more sampling sets of biomarker data; whereinafter each collection of the one or more sampling sets of biomarkerdata, said processor performs the processes of: determining a biomarkersampling parameter from the one or more sampling sets of biomarker datawhereby only these biomarker data are considered which are in compliancewith the one or more acceptance criterion; comparing the biomarkersampling parameter to a target biomarker range; calculating an insulinadjustment parameter associated with the biomarker sampling parameter inresponse to the biomarker sampling parameter falling outside the targetbiomarker range; adjusting an insulin dosage by an amount of the insulinadjustment parameter in response to the biomarker sampling parameterfalling outside the target biomarker range and in response to theinsulin dosage not exceeding maximum dosage; and exiting the testingmethod in response to the adjusted insulin dosage being optimized as anoptimized insulin dosage such that the optimized insulin dosage isadministered as the administered insulin dosage, otherwise repeatingwith a next sampling set of the one or more sampling sets of biomarkerdata, the optimized insulin dosage being achieved when the one or morebiomarker sampling parameters fall within the target biomarker range. 2.The testing method of claim 1 wherein the insulin dosage is optimizedwhen at least two consecutive biomarker sampling parameters fall withinthe target biomarker range.
 3. The testing method of claim 1 furthercomprising conducting a new testing method after achieving the optimizedinsulin dosage.
 4. The testing method of claim 1 wherein the collectionperiod for the one or more sampling sets of biomarker data is defined asmultiple sampling instances within a day, multiple sampling instanceswithin a week, multiple sampling instances within consecutive weeks, ormultiple sampling instances on consecutive days within a week.
 5. Thetesting method of claim 1 wherein each sampling instance of theplurality of sampling instances comprises the acceptable biomarkerreading and other contextual data associated with the acceptablebiomarker reading, wherein the contextual data is selected from thegroup consisting of a time of collection, a date of collection, a timewhen the last meal was consumed, and combinations thereof.
 6. Thetesting method of claim 1 further comprising collecting one or moreadditional sampling sets of biomarker data when the biomarker samplingparameter falls outside of the target biomarker range.
 7. The testingmethod of claim 1 further comprising informing the health care providerof any biomarker readings indicative of an adverse event that is ahyopglycemic event.
 8. The testing method of claim 1 wherein a biomarkerreading below a lower threshold is indicative of an adverse event. 9.The testing method of claim 1 further comprising contacting the healthcare provider for any biomarker readings indicative of an adverse event.10. The testing method of claim 1 wherein the administered insulindosage is decreased for any biomarker readings indicative of an adverseevent.
 11. The testing method of claim 1 wherein the one or moreacceptance criterion requires at least one of that a prescribed dosagebe administered throughout the collection period and that a fastingperiod before collection of the one or more sampling sets of biomarkerdata.
 12. The testing method of claim 1 further comprising meeting entrycriteria required for beginning to collect the one or more sampling setsof biomarker data.
 13. The testing method of claim 12 wherein the entrycriteria requires at least one of that the diabetic person be afflictedwith type 2 diabetes and that the diabetic person be limited to oraldiabetes medication prior to conducting the testing method.
 14. Thetesting method of claim 1 wherein the maximum insulin dosage is set bythe health care provider.
 15. The testing method of claim 1 wherein thebiomarker sampling parameter is determined by at least one of averagingsampling instances, summing the sampling instances, performing agraphical analysis on the sampling instances, and performing amathematical algorithm on the sampling set.
 16. The testing method ofclaim 1 wherein the calculating of the insulin adjustment parametercomprises locating the insulin adjustment parameter associated with thebiomarker parameter in an insulin adjustment parameter lookup table,utilizing an algorithm, and combinations thereof.
 17. The testing methodof claim 1 wherein the one or more acceptance criterion comprises one ormore considerations selected from the group consisting of: a patientdiet, fasting or eating regimen; a patient exercise regimen; a patientlifestyle; a patient sleep regimen; insulin dosage amounts; and timingof biomarker reading collection.
 18. The testing method of claim 1wherein the one or more acceptance criterion comprises an adherencecriteria, and in response to the diabetic person failing to meet theadherence criteria, the processor tags the failing as an adherence eventand informs the diabetic person via a display, then continues thetesting method, wherein in response to a determination that a number ofthe adherence events in a sample period are obtained or a total numberof the adherence events are reached in the testing method, the testingmethod is terminated.
 19. The testing method of claim 18 wherein saidprocessor performs the processes of categorizing, via the processor, theadherence events into tiers of either a lower weighted adherence eventin response to a determination that the adherence event will not causean error in at least one acceptable biomarker reading, or a higherweighted adherence event in response to a determination that theadherence event will cause the error in the at least one acceptablebiomarker reading, wherein the lower weighted adherence event is anevent that is not weighted as heavily as the higher weighted adherenceevent that affects the one or more sampling sets of biomarker data. 20.A testing method suitable for a diabetic person to optimize anadministered insulin dosage comprising: providing a structuredcollection procedure from a server of a health care provider to acollection device configured to guide the diabetic person through thestructured collection procedure and optimize the administered insulindosage, wherein the collection device comprises a meter configured tomeasure one or more selected biomarkers, a processor disposed inside themeter and coupled to a memory, wherein the memory stores the structuredcollection procedure provided from the server, and software havinginstructions that when executed by the processor causes the processor toinstruct the diabetic person to collect one or more sampling sets ofbiomarker data in accordance with the structured collection procedure,wherein the server is a central repository for a plurality of thestructured collection procedures; collecting one or more sampling setsof biomarker data by the collection device, wherein each of the one ormore sampling sets comprises a plurality of sampling instances recordedover a collection period and each sampling instance of the plurality ofsampling instances comprises an acceptable biomarker reading recordedupon compliance with one or more acceptance criterion based on at leastone of a schedule-based action or a patient-based action and that isapplied to the one or more sampling sets of biomarker data, wherein theone or more acceptance criterion require collection of the acceptablebiomarker reading by the diabetic patient at a time required by thecollection device; wherein after each collection of the one or moresampling sets of biomarker data, said processor performs the processesof: determining a biomarker sampling parameter from the one or moresampling sets of biomarker data whereby only these biomarker data areconsidered which are in compliance with the one or more acceptancecriterion; comparing the biomarker sampling parameter to a targetbiomarker range; calculating an insulin adjustment parameter associatedwith the biomarker sampling parameter in response to the biomarkersampling parameter falling outside the target biomarker range; adjustingan insulin dosage by an amount of the insulin adjustment parameter inresponse to the biomarker sampling parameter falling outside the targetbiomarker range and in response to the insulin dosage not exceedingmaximum dosage; and exiting the testing method in response to theadjusted insulin dosage being optimized as an optimized insulin dosagesuch that the optimized insulin dosage is administered as theadministered insulin dosage, otherwise repeating with a next samplingset of the one or more sampling sets of biomarker data, the optimizedinsulin dosage being achieved when the one or more biomarker samplingparameters fall within the target biomarker range.